{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VPoser\n",
    "The original body pose space of [SMPL](http://smpl.is.tue.mpg.de/) is not bounded to natural human pose space.\n",
    "That means you can put a vector value as the pose of a SMPL body model and get a broken body, which might not even look like a human.\n",
    "To address this issue we replace the original pose space of SMPL with VPoser's latent space (poZ) with known distribution and\n",
    "correspondence to natural human pose manifold.\n",
    "\n",
    "The original **body pose** of SMPL is composed of axis-angle representation of 21 joints which in total makes a vector of 63 elements; i.e. 3 rotation reprsentation for each joint.\n",
    "On the other hand, **body poZ**, VPoser's latent space representation for SMPL body, has in total 32 elements with a spherical Gaussian distribution.\n",
    "This means if one samples a 32 dimensional random vector from a Normal distribution and pass it through VPoser's decoder the result would be a viable human joint configuration in axis-angle representation.\n",
    "We introduce \"body poZ\" as a new representation of human body pose that is fully differentiable and can be used in an end-to-end deep learning pipeline.\n",
    "\n",
    "In this tutorial we demonstrate how to encode an original body pose into poZ, decode it back to pose and furthermore generate random novel poses from VPoser.\n",
    " \n",
    "First you need to obtain a trained VPoser and a variation of SMPL model, here we use SMPLx, from https://smpl-x.is.tue.mpg.de/downloads.\n",
    "\n",
    "Put the obtained VPoser and body models in a folder, here we assume respectively\n",
    "\n",
    "'GITHUB_CLONE_ROOT/human_body_prior/support_data/dowloads/vposer_vXX', and\n",
    "'GITHUB_CLONE_ROOT/human_body_prior/support_data/dowloads/models/smplx/GENDER/model.npz'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up environment\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "from body_visualizer.tools.vis_tools import render_smpl_params\n",
    "from body_visualizer.tools.vis_tools import imagearray2file\n",
    "from body_visualizer.tools.vis_tools import show_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../support_data/dowloads/vposer_v2_05\n",
      "../support_data/dowloads/models/smplx/neutral/model.npz\n",
      "../support_data/dowloads/amass_sample.npz\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#This tutorial requires 'vposer_v2_05'\n",
    "\n",
    "from os import path as osp\n",
    "support_dir = '../support_data/dowloads'\n",
    "expr_dir = osp.join(support_dir,'vposer_v2_05') #'TRAINED_MODEL_DIRECTORY'  in this directory the trained model along with the model code exist\n",
    "bm_fname =  osp.join(support_dir,'models/smplx/neutral/model.npz')#'PATH_TO_SMPLX_model.npz'  obtain from https://smpl-x.is.tue.mpg.de/downloads\n",
    "sample_amass_fname = osp.join(support_dir, 'amass_sample.npz')# a sample npz file from AMASS\n",
    "\n",
    "print(expr_dir)\n",
    "print(bm_fname)\n",
    "print(sample_amass_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Loading SMPLx Body Model\n",
    "from human_body_prior.body_model.body_model import BodyModel\n",
    "\n",
    "bm = BodyModel(bm_fname=bm_fname).to('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading VPoser Body Pose Prior\n",
    "from human_body_prior.tools.model_loader import load_model\n",
    "from human_body_prior.models.vposer_model import VPoser\n",
    "vp, ps = load_model(expr_dir, model_code=VPoser,\n",
    "                              remove_words_in_model_weights='vp_model.',\n",
    "                              disable_grad=True)\n",
    "vp = vp.to('cuda')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding a body_pose (pose>poZ)\n",
    "We will load an [AMASS](http://amass.is.tue.mpg.de/) sample and place the body pose on the right device for batch processing. To learn more on AMASS data loading refer to [link](https://github.com/nghorbani/amass/blob/master/notebooks/01-AMASS_Visualization.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amass_body_pose.shape torch.Size([500, 63])\n"
     ]
    }
   ],
   "source": [
    "# Prepare the pose_body from amass sample\n",
    "amass_body_pose = np.load(sample_amass_fname)['poses'][:, 3:66]\n",
    "amass_body_pose = torch.from_numpy(amass_body_pose).type(torch.float).to('cuda')\n",
    "print('amass_body_pose.shape', amass_body_pose.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amass_body_poZ.shape torch.Size([500, 32])\n"
     ]
    }
   ],
   "source": [
    "amass_body_poZ = vp.encode(amass_body_pose).mean\n",
    "print('amass_body_poZ.shape', amass_body_poZ.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decoding a body_poZ (poZ>pose)\n",
    "We will decode the same poZ in order to reconstruct the pose and will visualize it for a random frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amass_body_pose_rec.shape torch.Size([500, 32])\n"
     ]
    }
   ],
   "source": [
    "amass_body_pose_rec = vp.decode(amass_body_poZ)['pose_body'].contiguous().view(-1, 63)\n",
    "print('amass_body_pose_rec.shape', amass_body_poZ.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 63])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Let's visualize the original pose and the reconstructed one:\n",
    "\n",
    "t = np.random.choice(len(amass_body_pose))\n",
    "\n",
    "all_pose_body = torch.stack([amass_body_pose[t], amass_body_pose_rec[t]])\n",
    "\n",
    "all_pose_body.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "images = render_smpl_params(bm, {'pose_body':all_pose_body}).reshape(1,2,1,400,400,3)\n",
    "img = imagearray2file(images)\n",
    "show_image(img[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The closer above two bodies look similar to each other, the more successful is VPoser is in reconstructing the original data space body pose parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate novel body poses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "poZ_body_sample.shape torch.Size([1, 32])\n",
      "pose_body.shape torch.Size([1, 63])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sample a 32 dimensional vector from a Normal distribution\n",
    "poZ_body_sample = torch.from_numpy(np.random.randn(1,32).astype(np.float32)).to('cuda')\n",
    "pose_body = vp.decode(poZ_body_sample)['pose_body'].contiguous().view(-1, 63)\n",
    "\n",
    "print('poZ_body_sample.shape', poZ_body_sample.shape)\n",
    "print('pose_body.shape', pose_body.shape)\n",
    "\n",
    "images = render_smpl_params(bm, {'pose_body':pose_body}).reshape(1,1,1,400,400,3)\n",
    "img = imagearray2file(images)\n",
    "show_image(img[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we drawn a sample from a 32 dimensional Normal distribution and decoded its value to a full 63 dimensional SMPL body pose vector. The generated image shows the corresponding rendered body. Fo an advanced tutorial on generating poses with VPoser refer to [Link](notebooks/vposer_sampling.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}